More Like this

1. Circumventing traps in analog quantum machine learning algorithms through co-design

   https://doi.org/10.1063/5.0235279  

   Bravo, Rodrigo_Araiza; Ponce, Jorge_Garcia; Hu, Hong-Ye; Yelin, Susanne_F (December 2024, APL Quantum)

   Quantum machine learning algorithms promise to deliver near-term, applicable quantum computation on noisy, intermediate-scale systems. While most of these algorithms leverage quantum circuits for generic applications, a recent set of proposals, called analog quantum machine learning (AQML) algorithms, breaks away from circuit-based abstractions and favors leveraging the natural dynamics of quantum systems for computation, promising to be noise-resilient and suited for specific applications such as quantum simulation. Recent AQML studies have called for determining best ansatz selection practices and whether AQML algorithms have trap-free landscapes based on theory from quantum optimal control (QOC). We address this call by systematically studying AQML landscapes on two models: those admitting black-boxed expressivity and those tailored to simulating a specific unitary evolution. Numerically, the first kind exhibits local traps in their landscapes, while the second kind is trap-free. However, both kinds violate QOC theory’s key assumptions for guaranteeing trap-free landscapes. We propose a methodology to co-design AQML algorithms for unitary evolution simulation using the ansatz’s Magnus expansion. Our methodology guarantees the algorithm has an amenable dynamical Lie algebra with independently tunable terms. We show favorable convergence in simulating dynamics with applications to metrology and quantum chemistry. We conclude that such co-design is necessary to ensure the applicability of AQML algorithms. 

   more » « less
2. Distributed Quantum Learning with co-Management in a Multi-tenant Quantum System

   https://doi.org/10.1109/BigData59044.2023.10386676  

   D’Onofrio, Anthony; Hossain, Amir; Santana, Lesther; Machlovi, Naseem; Stein, Samuel; Liu, Jinwei; Li, Ang; Mao, Ying (December 2023, IEEE International Conference on Big Data (BigData))
3. On the Co-Design of Quantum Software and Hardware

   https://doi.org/10.1145/3477206.3477464  

   Li, Gushu; Wu, Anbang; Shi, Yunong; Javadi-Abhari, Ali; Ding, Yufei; Xie, Yuan (September 2021, Proceedings of the Eight Annual ACM International Conference on Nanoscale Computing and Communication)

   null (Ed.)
4. Quantum Neural Network Extraction Attack via Split Co-Teaching

   https://doi.org/10.1109/ICASSPW65056.2025.11011056  

   Fu, Zhenxiao; Chen, Fan (April 2025, IEEE)
5. Predictive study of Mn- and Co-based van der Waals compounds for spintronic and quantum applications

   https://doi.org/10.1007/s10853-025-10972-w  

   Yaqoob, Al-Suwaychit_Fadhil_Noori; Nourbakhsh, Zahra; Vashaee, Daryoosh (May 2025, Journal of Materials Science)

   Abstract This study investigates the structural, electronic, and magnetic properties of XBr₂, XI₂, and XBrI (X = Mn, Co) compounds using density functional theory, incorporating spin–orbit coupling and the GGA + U framework. Cohesive and formation energy calculations reveal that MnBr₂is most stable in the ferromagnetic phase, while the other compounds favor antiferromagnetic ordering. The inclusion of the effective Coulomb screening potential (U_eff) enhances the localization of 3d orbitals, leading to increased magnetic moments. Electronic structure analyses show that most compounds transition to semiconducting behavior in the antiferromagnetic phase—except CoI₂—while MnBr₂, CoBr₂, and CoI₂exhibit half-metallicity in the ferrimagnetic phase. In the antiferromagnetic phase, MnBr₂, MnI₂, and MnBrI display topological Dirac-like points between theRand Γ points, suggesting the presence of massless fermions and enabling phenomena such as the quantum Hall effect and ultra-high carrier mobility. The computational results are consistent with available experimental data, highlighting the potential of Mn- and Co-based van der Waals compounds for spintronic and quantum applications. 

   more » « less